The purpose of this publication is the long-term forecasting of the landslide processes activation for the territory of the Precarpathian depression within the Chernivtsi region, taking into account the complex effect of natural factors. Methodology. Based on statistical analysis and processing...

Investigating the regime of groundwater levels is one of the main stages of hydrogeological research, which allows quantitative characterization of the process of formation and change of hydrogeological conditions in time. To date, there has been a problem in filling the series of groundwater...

The purpose of the article is to carry out a complex analysis of conditions and natural factors forming the hydrogeological regime as well as to integrate the existing information and groundwater monitoring data in Ivano-Frankivsk region for further creating of the unified automated system to...

Nowadays, within the Carpathian region (Ukraine), the main factor characteristics determining the temporal prediction shall be the sunspot activity (the Wolf number) and seismic activity (the total energy released in earthquakes). Engineering-geological zoning, as a method of representative analysis of geological data, is used as a spatial component, which includes the grouped structural-tectonic, geomorphological, morphometric and climatic features of a specific engineering-geological area. The article proposes a new approach to the temporal prediction within individual engineering-geological areas. For the selected study area, we have performed a statistical analysis of factor characteristics, which are a measure of factor determinability to choose the optimal model of long-standing temporal analysis within the engineering-geological areas of the Carpathian region of Ukraine. To substantiate the methodology of spatial-temporal analysis, we have patterned the data regarding the landslide activity, sunspot activity (the Wolf number), groundwater levels, an average of annual temperature, seismic activity (total energy released in earthquakes), an average of annual rainfall within the certain engineering-geological area of the Transcarpathian inner depression. Statistical analysis of data based on the consistent study of the factor characteristics. At the same time, we have performed the correlation analysis with the allocation of autocorrelation functions as a basis for considering temporal dynamic features of each of the factor characteristics. Based on them, we have drawn up the periodograms of main climatic factors such as an average of annual rainfall, an average of annual temperatures and an average of groundwater level. Under the spectral analysis, we distinguished the main periodicities by summarizing the data at the point of landslide manifestation and three points in the form of a function of the temporal series. There was a comparison of the temporal series based on PREDICT function of MathCad integrated mathematical package and neural network prediction. We determined that the basic frequency components of temporal activation dynamism of landslide processes for certain engineering-geological area of the Carpathian region of Ukraine can be divided into the cycles having a period of small and large activation. Under the long-term prediction, the main cycle of the landslide activity is 28 years. In particular, the neural network prediction distinguished the highest probability of the landslides in 2020-2021 of the first half cycle (2019-2023) and 2028-2030 of the second half cycle (2027-2032). The prediction based on Predict function highlighted the period of 2020 - 2030. Therefore, the next massive activation of the landslides will be in 2020-2023 and in 2028-2030.

The objective of the present study is to undertake a quantitative assessment of the spa-tial impact of tourism on natural complexes in the Carpathian region. This is achieved by employing geostatistical modelling methods. The present study deviates from con-ventional buffer methodologies insofar as it employs an integrated algorithm founded upon Kernel Density Estimation (KDE), thereby amalgamating multivariate analysis with detailed land use classification (LULC). In the analysis, each of the seven factors ̶ hydro-graphic network, relief, transport accessibility, hotels, tourist routes, land use types and nature conservation areas ̶ is standardised on a scale of 1–10 and synthesised into a single model of the vulnerability of natural landscapes to tourist pressure. The spatial integration of the factors demonstrates that areas exhibiting high and very high vul-nerability encompassed a total area of more than 310 km², constituting approximately 34% of the total area under consideration. These areas are predominantly concentrated within mountain valleys and along major transport axes. The findings demonstrate the substantial analytical superiority of KDE in comparison to buffer models, owing to its capacity to accommodate continuous impact gradients and its strong alignment with actual urbanisation patterns. The proposed approach can be applied to spatial planning for sustainable tourism and environmental monitoring in mountain regions.

The Carpathian region, to which the research area belongs, is one of the most forested areas of Ukraine, and at the same time the most anthropogenically loaded. Studying the vegetation cover of the territories is currently one of the most important parameters that allows us to assess the state of the environment. The paper analyzes the change in the state of vegetation based on the NDVI index using Google Earth Engine (GEE) for the period between 2018 and 2023, with the construction of a difference index. The satellite image interpretation data allowed us to summarise the dynamics of the vegetation index change for two similar periods, in particular, for 2018 it is 0.464, for 2023 it is 0.330. The average difference value for the study area is 0.229. Taking into account dynamic climate change and its parameters such as precipitation, air temperature, surface temperature, wind speed, NDVI and NDWI indices, a correlation analysis was performed to identify the relationship between changes in vegetation cover and weather conditions. Statistical analysis of the data allowed us to identify the main groups of factors that have the greatest impact on the NDVI index, namely temperature and precipitation. However, the NDWI moisture index is in a clear inverse correlation, which confirms the significant impact of global temperatures on the unevenness of precipitation, especially during the 2018 floods. It is worth noting that the movement of air masses, which was determined through the average annual wind speed, gives an understanding of how changes in temperature above the ground affect the moisture content of vegetation.